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Advancements and Challenges in Space Exploration and Cyber Threat Mitigation Techniques: A Comprehensive Review 57

Advancements and Challenges in Space Exploration and Cyber T...

57th IAA Symposium on Safety, Quality and Knowledge Management in Space Activities at the 75th International Astronautical Congress, IAC 2024

作者： Almousa, May M. Abd El-Latif, Ahmed A. Information Technology Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (纸本)9798331312275

Space exploration has been revolutionized by deep learning (DL) and machine learning (ML) technologies, enabling advanced data analysis, spacecraft navigation, and mission planning. However, the increasing reliance on digital systems also introduces new cybersecurity vulnerabilities. This research presents a comprehensive literature review of how ML and DL can enhance mission effectiveness while mitigating these risks. By examining over 50 research articles, we identify significant advancements and challenges in space mission cybersecurity, with a particular focus on DL for threat detection, risk assessment, and system protection. Our findings highlight the potential of DL to address emerging cybersecurity threats and improve the overall security and efficiency of space missions. This work aims to provide a foundation for developing robust and secure AI-powered systems for future space exploration endeavors. Copyright © 2024 by the International Astronautical Federation (IAF).

关键词： Cyber attacks

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Explainable Artificial Intelligence Solution for Online Retail

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computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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VCOACH: A Virtual Coaching System Based on Visual Streaming

VCOACH: A Virtual Coaching System Based on Visual Streaming

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International Neural Network Society Workshop on Deep Learning Innovations and Applications, INNS DLIA 2023

作者： Youssef, Fatma Parque, Victor Gomaa, Walid Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria21934 Egypt Department of Modern Mechanical Engineering Waseda University Tokyo169-8050 Japan Cyber-Physical Systems Lab. Egypt-Japan University of Science and Technology Alexandria21934 Egypt Faculty of Engineering Alexandria University Alexandria21544 Egypt

A virtual coaching system is key for monitoring the performance during exercise regimes and rendering timely feedback to avoid potential physical harm. Ideally, the core technologies behind such a system imply exercise recognition/assessment for performance evaluation and subject identification for retrieval/update of personal profiles. In this work, we develop techniques based on learning models for inferences regarding exercise recognition, prediction of the subject's biometrics, exercise evaluation, and simultaneous recognition and evaluation. We address four indoor exercises: squats, push-ups, shoulder-press, and lunges. Different sorts of exercise evaluation are provided: (1) the overall-binary assessment, with a detailed assessment rendering the exact mistakes performed, and (2) the score-based evaluation giving a metric in the range of 0-10 for each aspect of the exercise. Thermal and RGB imagery are examined with various descriptors, such as raw images, Gait Energy Image (GEI), and skeletal pose, with a comparison between their performance. The GEI descriptor extracted from RGB imagery is introduced for exercise recognition and evaluation tasks achieving remarkable results. The GEI descriptor is further utilized for subject recognition and verification from the performed exercise showing the ability of subject discrimination from the way of exercising. Consequently, we introduce new kinds of biometrics based on indoor exercising inspired by the notion that individuals are recognizable from the fingerprint motion dynamics in exercise regimes. To the best of our knowledge, our work is the first to use GEI in such directions and show its remarkable effectiveness. © 2023 The Authors. Published by Elsevier B.V.

关键词： Biometrics

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LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Expression in Vision-Language Models

arXiv

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arXiv 2024年

作者： Wang, Jingyi Ju, Jianzhong Luan, Jian Deng, Zhidong Department of Computer Science and Technology Tsinghua University Beijing China Xiaomi AI Lab Beijing China

Recent advances in large vision-language models (VLMs) typically employ vision encoders based on the Vision Transformer (ViT) architecture. The division of the images into patches by ViT results in a fragmented perception, thereby hindering the visual understanding capabilities of VLMs. In this paper, we propose an innovative enhancement to address this limitation by introducing a Scene Graph Expression (SGE) module in VLMs. This module extracts and structurally expresses the complex semantic information within images, thereby improving the foundational perception and understanding abilities of VLMs. Extensive experiments demonstrate that integrating our SGE module significantly enhances the VLM’s performance in vision-language tasks, indicating its effectiveness in preserving intricate semantic details and facilitating better visual understanding. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Expediting Distributed GNN Training with Feature-only Partition and Optimized Communication Planning

Expediting Distributed GNN Training with Feature-only Partit...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Bingqian Du Jun Liu Ziyue Luo Chuan Wu Qiankun Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering The Ohio State University USA Department of Computer Science The University of Hong Kong Hong Kong School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Feature-only partition of large graph data in distributed Graph Neural Network (GNN) training offers advantages over commonly adopted graph structure partition, such as minimal graph preprocessing cost and elimination of cross-worker subgraph sampling burdens. Nonetheless, performance bottleneck of GNN training with feature-only partitions still largely lies in the substantial communication overhead due to cross-worker feature fetching. To reduce the communication overhead and expedite distributed training, we first investigate and answer two key questions on convergence behaviors of GNN model in feature-partition based distribute GNN training: 1) As no worker holds a complete copy of each feature, can gradient exchange among workers compensate for the information loss due to incomplete local features? 2) If the answer to the first question is negative, is feature fetching in every training iteration of the GNN model necessary to ensure model convergence? Based on our theoretical findings on these questions, we derive an optimal communication plan that decides the frequency for feature fetching during the training process, taking into account bandwidth levels among workers and striking a balance between model loss and training time. Extensive evaluation demonstrates consistent results with our theoretical analysis, and the effectiveness of our proposed design.

关键词： Training Time-frequency analysis Costs Computational modeling Distributed databases Bandwidth Graph neural networks

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Evaluating Keyphrase Extraction Methods for Clustering Influenza-Related Scientific Papers

Evaluating Keyphrase Extraction Methods for Clustering Influ...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Majid Forghani Artyom Firstkov Mohammad. M. AlyanNezhadi Kazem Forghani Mathematical Programming Lab. Krasovskii Institute of Mathematics & Mechanics Yekaterinburg Russia Department of Science University of Science and Technology of Mazandaran Behshahr Iran Computer Science Department Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781665476249

The number of annual scientific publications is growing year by year, which has led to the accumulation and formation of large databases. This increases the complexity of the search for relevant articles. Modern search engines leverage keyphrases to improve the performance of search results. Keyphrases (or keywords) are a set of single or multi-word expressions which provide a very compact summary of contents and describe the overall topic of a document. Implementation of keyphrase extraction can be varied for specific-domain databases. This motivated us to evaluate these methods on a database of influenza scientific literature. In this work, we considered 9 well known methods for extracting keyphrases. Our preliminary results show txhat graph-based methods which employ topics outperform others using our database. We also determined that influenza-related papers can be grouped into three general topics: public health & medical care; molecular biology & immunology; and phylogenetic & epidemiological studies.

关键词： Databases Immunology Biological system modeling Influenza Search engines Phylogeny Molecular biology

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Exploring Explainable AI for Enhanced Depression Prediction in Mental Health

Exploring Explainable AI for Enhanced Depression Prediction ...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： Ashly Ann Jo Ebin Deni Raj Angel Susan Vino Parvathy V Menon Department of Computer Science & Engineering FACTS-H Lab Indian Institute of Information Technology Kottayam India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Depression is a prevalent mental health condition affecting millions of individuals worldwide. This paper presents a comprehensive analysis using machine learning to predict depression, a prevalent mental health issue affecting millions worldwide. The dataset employed for this study includes a range of mental health conditions such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, and alcohol use disorders, investigating their correlations with depression. We explore these relationships and develop predictive models to forecast the likelihood of depression. The study evaluates the performance of various machine learning algorithms, with the Random Forest algorithm demonstrating the highest accuracy rate of 99.30%. Furthermore, we delve into model interpretability by applying techniques like LIME and SHAP to explain the predictions. Our findings contribute significantly to the understanding and prediction of depression, offering valuable insights that could assist healthcare professionals in the early detection and proactive management of this condition.

关键词： Training Technological innovation Machine learning algorithms Accuracy Mental health Predictive models Depression Prediction algorithms Random forests Streams

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Subnetwork-To-Go: Elastic Neural Network with Dynamic Training and Customizable Inference

Subnetwork-To-Go: Elastic Neural Network with Dynamic Traini...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kai Li Yi Luo Tencent AI Lab Shenzhen China Department of Computer Science and Technology BNRist Tsinghua University China

Deploying neural networks to different devices or platforms is in general challenging, especially when the model size is large or model complexity is high. Although there exist ways for model pruning or distillation, it is typically required to perform a full round of model training or finetuning procedure in order to obtain a smaller model that satisfies the model size or complexity constraints. Motivated by recent works on dynamic neural networks, we propose a simple way to train a large network and flexibly extract a subnetwork from it given a model size or complexity constraint during inference. We introduce a new way to allow a large model to be trained with dynamic depth and width during the training phase, and after the large model is trained we can select a subnetwork from it with arbitrary depth and width during the inference phase with a relatively better performance compared to training the subnetwork independently from scratch. Experiment results on a music source separation model show that our proposed method can effectively improve the separation performance across different subnetwork sizes and complexities with a single large model, and training the large model takes significantly shorter time than training all the different subnetworks.

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Performance Analysis and Predictive Modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study

Performance Analysis and Predictive Modeling of MPI Collecti...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Manideep Raya V S R S Reddy Sanjay Ramapogu Raju Girish K K Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms—Flat Tree, Chain, and Binary Tree—by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical.

关键词： Program processors Network topology Scalability Message passing Clustering algorithms Parallel processing Predictive models Prediction algorithms Topology Performance analysis

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Apollo: Band-sequence Modeling for High-Quality Audio Restoration

arXiv

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arXiv 2024年

作者： Li, Kai Luo, Yi Department of Computer Science and Technology Tsinghua University Beijing China Tencent AI Lab Shenzhen China

Audio restoration has become increasingly significant in modern society, not only due to the demand for high-quality auditory experiences enabled by advanced playback devices, but also because the growing capabilities of generative audio models necessitate high-fidelity audio. Typically, audio restoration is defined as a task of predicting undistorted audio from damaged input, often trained using a GAN framework to balance perception and distortion. Since audio degradation is primarily concentrated in mid- and high-frequency ranges, especially due to codecs, a key challenge lies in designing a generator capable of preserving low-frequency information while accurately reconstructing high-quality mid- and high-frequency content. Inspired by recent advancements in high-sample-rate music separation, speech enhancement, and audio codec models, we propose Apollo, a generative model designed for high-sample-rate audio restoration. Apollo employs an explicit frequency band split module to model the relationships between different frequency bands, allowing for more coherent and higher-quality restored audio. Evaluated on the MUSDB18-HQ and MoisesDB datasets, Apollo consistently outperforms existing SR-GAN models across various bit rates and music genres, particularly excelling in complex scenarios involving mixtures of multiple instruments and vocals. Apollo significantly improves music restoration quality while maintaining computational efficiency. The source code for Apollo is publicly available at https://***/JusperLee/Apollo. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

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